This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
日本語の概要は準備中です。原文の説明を表示しています。
Ensure thorough validation, error recovery, and transparent reasoning in research tasks with multiple tool calls
インストールする前に、エージェントに与えられる指示の中身を確認できます。
This skill addresses common failures in multi-step research tasks: unhandled tool errors, missing validation, opaque reasoning, and premature conclusions. It provides structured protocols for source validation, error recovery, and thinking transparency that significantly improves research quality and reliability.
After (Pattern): 'Search returned 15 results on context engineering. Evaluating relevance: Liu et al. (2024) appears most authoritative on 'lost in the middle' phenomenon; Anthropic documentation likely has current context window specs; Patel (2023) covers RAG best practices. Ranking these as top 3 priorities. Reading top result first. If the primary source fails (URL error), will try backup search for correct documentation URL and note the gap in final report.'
After (Pattern): Tool returned 404 for Anthropic URL. Thinking: 'Primary source failed. Fallback: search for alternative Anthropic documentation URL or find archived version. If unavailable, note context window data from secondary sources only and add disclaimer about verification status.' Then: 'Cross-validated Claude context window: Anthropic blog (successfully read) and two developer documentation sources agree on 200K. Confident in this claim.' Source tracking table shows: Anthropic URL (failed, backup used), Blog (success), Dev docs (success).
Complex research tasks with multiple tools (6+) and multi-step reasoning chains typically achieve scores in the 65-75 range. This is not a limitation of the prompt but reflects:
Focus on relative improvement and pattern elimination rather than absolute scores. A 5-10% improvement from optimization is significant for complex tasks.
Generated: 2026-01-11 Source: Reasoning Trace Optimizer Optimization Iterations: 10 Best Score Achieved: 72/100 (iteration 4) Final Score: 70.0/100 Score Improvement: 67.6 → 70.0 (+3.6%)
まだレビューはありません。使ってみた感想をお寄せください。
概要と使いどころ
This skill should be used for advanced LLM evaluation: LLM-as-judge systems, direct scoring, pairwise comparison, rubric calibration, evaluator bias mitigation, confidence scoring, and automated quality assessment.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when modeling agent mental states with BDI concepts: beliefs, desires, intentions, RDF-to-belief transformations, rational agency traces, cognitive agents, BDI ontologies, and neuro-symbolic AI integration.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used for book-to-SFT pipelines: ePub extraction, literary segmentation, author-voice dataset construction, style-transfer training, LoRA workflows, and model evaluation for voice replication.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used when long-running agent sessions need context compression, structured summarization, compaction, token-per-task optimization, or durable handoff summaries that preserve decisions, files, risks, and next actions.
日本語の概要は準備中です。原文の説明を表示しています。
This skill should be used for diagnosing and mitigating context degradation: lost-in-middle failures, context poisoning, context clash, context confusion, attention-pattern issues, and agent performance degradation caused by accumulated or conflicting context.
日本語の概要は準備中です。原文の説明を表示しています。
A comprehensive collection of Agent Skills for context engineering, harness engineering, multi-agent architectures, and production agent systems. Use when building, optimizing, evaluating, or debugging agent systems that require effective context management and reliable operating loops.
日本語の概要は準備中です。原文の説明を表示しています。